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04738nmm a2200385 u 4500 |
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EBX01000000000000001259846 |
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221107 ||| eng |
| 020 |
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|a 9783031146343
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| 100 |
1 |
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|a Ghosh, Chandril
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| 245 |
0 |
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|a Data Analysis with Machine Learning for Psychologists
|h Elektronische Ressource
|b Crash Course to Learn Python 3 and Machine Learning in 10 hours
|c by Chandril Ghosh
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| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2022, 2022
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| 300 |
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|a XIII, 161 p. 121 illus., 113 illus. in color
|b online resource
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| 505 |
0 |
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|a Introduction -- Step 1:Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note. Python Programming -- Step 2:Data Pre-Processing -- Step 3: Data Analysis with Machine Learning -- End Note.
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| 653 |
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|a Psychology—Methodology
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| 653 |
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|a Cognitive science
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| 653 |
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|a Social sciences—Statistical methods
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| 653 |
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|a Behavioral Sciences and Psychology
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| 653 |
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|a Mental Health
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| 653 |
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|a Business—Data processing
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| 653 |
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|a Psychology
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| 653 |
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|a Psychological Methods
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| 653 |
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|a Cognitive Science
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| 653 |
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|a Business Analytics
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| 653 |
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|a Mental health
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| 653 |
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|a Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
0 |
|a 10.1007/978-3-031-14634-3
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-031-14634-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 150
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| 520 |
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|a The power of data drives the digital economy of the 21st century. It has been argued that data is as vital a resource as oil was during the industrial revolution. An upward trend in the number of research publications using machine learning in some of the top journals in combination with an increasing number of academic recruiters within psychology asking for Python knowledge from applicants indicates a growing demand for these skills in the market. While there are plenty of books covering data science, rarely, if ever, books in the market address the need of social science students with no computer science background. They are typically written by engineers or computer scientists for people of their discipline. As a result, often such books are filled with technical jargon and examples irrelevant to psychological studies or projects. In contrast, this book was written by a psychologist in a simple, easy-to-understand way that is brief and accessible. The aim for this book was to make the learning experience on this topic as smooth as possible for psychology students/researchers with no background in programming or data science. Completing this book will also open up an enormous amount of possibilities for quantitative researchers in psychological science, as it will enable them to explore newer types of research questions.
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